{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data():\n",
    "    df = pd.read_csv(\"car_price.csv\")\n",
    "    return df\n",
    "df = get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>201</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 145e (sw)</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 144ea</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>203</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 244dl</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>204</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 246</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 264gl</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     car_ID  symboling                   CarName fueltype aspiration  \\\n",
       "0         1          3        alfa-romero giulia      gas        std   \n",
       "1         2          3       alfa-romero stelvio      gas        std   \n",
       "2         3          1  alfa-romero Quadrifoglio      gas        std   \n",
       "3         4          2               audi 100 ls      gas        std   \n",
       "4         5          2                audi 100ls      gas        std   \n",
       "..      ...        ...                       ...      ...        ...   \n",
       "200     201         -1           volvo 145e (sw)      gas        std   \n",
       "201     202         -1               volvo 144ea      gas      turbo   \n",
       "202     203         -1               volvo 244dl      gas        std   \n",
       "203     204         -1                 volvo 246   diesel      turbo   \n",
       "204     205         -1               volvo 264gl      gas      turbo   \n",
       "\n",
       "    doornumber      carbody drivewheel enginelocation  wheelbase  ...  \\\n",
       "0          two  convertible        rwd          front       88.6  ...   \n",
       "1          two  convertible        rwd          front       88.6  ...   \n",
       "2          two    hatchback        rwd          front       94.5  ...   \n",
       "3         four        sedan        fwd          front       99.8  ...   \n",
       "4         four        sedan        4wd          front       99.4  ...   \n",
       "..         ...          ...        ...            ...        ...  ...   \n",
       "200       four        sedan        rwd          front      109.1  ...   \n",
       "201       four        sedan        rwd          front      109.1  ...   \n",
       "202       four        sedan        rwd          front      109.1  ...   \n",
       "203       four        sedan        rwd          front      109.1  ...   \n",
       "204       four        sedan        rwd          front      109.1  ...   \n",
       "\n",
       "     enginesize  fuelsystem  boreratio  stroke compressionratio horsepower  \\\n",
       "0           130        mpfi       3.47    2.68              9.0        111   \n",
       "1           130        mpfi       3.47    2.68              9.0        111   \n",
       "2           152        mpfi       2.68    3.47              9.0        154   \n",
       "3           109        mpfi       3.19    3.40             10.0        102   \n",
       "4           136        mpfi       3.19    3.40              8.0        115   \n",
       "..          ...         ...        ...     ...              ...        ...   \n",
       "200         141        mpfi       3.78    3.15              9.5        114   \n",
       "201         141        mpfi       3.78    3.15              8.7        160   \n",
       "202         173        mpfi       3.58    2.87              8.8        134   \n",
       "203         145         idi       3.01    3.40             23.0        106   \n",
       "204         141        mpfi       3.78    3.15              9.5        114   \n",
       "\n",
       "     peakrpm citympg  highwaympg    price  \n",
       "0       5000      21          27  13495.0  \n",
       "1       5000      21          27  16500.0  \n",
       "2       5000      19          26  16500.0  \n",
       "3       5500      24          30  13950.0  \n",
       "4       5500      18          22  17450.0  \n",
       "..       ...     ...         ...      ...  \n",
       "200     5400      23          28  16845.0  \n",
       "201     5300      19          25  19045.0  \n",
       "202     5500      18          23  21485.0  \n",
       "203     4800      26          27  22470.0  \n",
       "204     5400      19          25  22625.0  \n",
       "\n",
       "[205 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   car_ID            205 non-null    int64  \n",
      " 1   symboling         205 non-null    int64  \n",
      " 2   CarName           205 non-null    object \n",
      " 3   fueltype          205 non-null    object \n",
      " 4   aspiration        205 non-null    object \n",
      " 5   doornumber        205 non-null    object \n",
      " 6   carbody           205 non-null    object \n",
      " 7   drivewheel        205 non-null    object \n",
      " 8   enginelocation    205 non-null    object \n",
      " 9   wheelbase         205 non-null    float64\n",
      " 10  carlength         205 non-null    float64\n",
      " 11  carwidth          205 non-null    float64\n",
      " 12  carheight         205 non-null    float64\n",
      " 13  curbweight        205 non-null    int64  \n",
      " 14  enginetype        205 non-null    object \n",
      " 15  cylindernumber    205 non-null    object \n",
      " 16  enginesize        205 non-null    int64  \n",
      " 17  fuelsystem        205 non-null    object \n",
      " 18  boreratio         205 non-null    float64\n",
      " 19  stroke            205 non-null    float64\n",
      " 20  compressionratio  205 non-null    float64\n",
      " 21  horsepower        205 non-null    int64  \n",
      " 22  peakrpm           205 non-null    int64  \n",
      " 23  citympg           205 non-null    int64  \n",
      " 24  highwaympg        205 non-null    int64  \n",
      " 25  price             205 non-null    float64\n",
      "dtypes: float64(8), int64(8), object(10)\n",
      "memory usage: 41.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# 没用空值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 类别数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = df.select_dtypes(include=\"object\").columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CarName', 'fueltype', 'aspiration', 'doornumber', 'carbody',\n",
       "       'drivewheel', 'enginelocation', 'enginetype', 'cylindernumber',\n",
       "       'fuelsystem'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CarName 汽车名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toyota corona         6\n",
       "toyota corolla        6\n",
       "peugeot 504           6\n",
       "subaru dl             4\n",
       "toyota mark ii        3\n",
       "                     ..\n",
       "maxda glc deluxe      1\n",
       "renault 5 gtl         1\n",
       "nissan fuga           1\n",
       "honda civic (auto)    1\n",
       "peugeot 504 (sw)      1\n",
       "Name: CarName, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CarName'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 车名好像是品牌+车型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['brand'] = df['CarName'].apply(lambda x: x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toyota         31\n",
       "nissan         17\n",
       "mazda          15\n",
       "mitsubishi     13\n",
       "honda          13\n",
       "subaru         12\n",
       "volvo          11\n",
       "peugeot        11\n",
       "dodge           9\n",
       "volkswagen      9\n",
       "bmw             8\n",
       "buick           8\n",
       "plymouth        7\n",
       "audi            7\n",
       "saab            6\n",
       "isuzu           4\n",
       "porsche         4\n",
       "chevrolet       3\n",
       "jaguar          3\n",
       "alfa-romero     3\n",
       "renault         2\n",
       "vw              2\n",
       "maxda           2\n",
       "porcshce        1\n",
       "mercury         1\n",
       "Nissan          1\n",
       "vokswagen       1\n",
       "toyouta         1\n",
       "Name: brand, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 汽车品牌存在重复和缩写，统一汽车品牌\n",
    "df['brand'] = df['brand'].replace({\n",
    "                'Nissan': 'nissan', 'maxda': 'mazda', 'porcshce': 'porsche', \n",
    "                'toyouta': 'toyota', 'vokswagen': 'volkswagen','vw': 'volkswagen', })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toyota         32\n",
       "nissan         18\n",
       "mazda          17\n",
       "honda          13\n",
       "mitsubishi     13\n",
       "subaru         12\n",
       "volkswagen     12\n",
       "volvo          11\n",
       "peugeot        11\n",
       "dodge           9\n",
       "bmw             8\n",
       "buick           8\n",
       "plymouth        7\n",
       "audi            7\n",
       "saab            6\n",
       "porsche         5\n",
       "isuzu           4\n",
       "alfa-romero     3\n",
       "jaguar          3\n",
       "chevrolet       3\n",
       "renault         2\n",
       "mercury         1\n",
       "Name: brand, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "sns.countplot(df['brand'], ax=ax)\n",
    "plt.xticks(rotation=90)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**数量最多的是Toyota丰田汽车，第二多的是Nissan是日产，第三的是马自达,第四是本田，第五是三菱，第六斯巴鲁都是日系车**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fueltype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gas       185\n",
       "diesel     20\n",
       "Name: fueltype, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fueltype'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**燃油类型以汽油为主，大约10%的车烧的是柴油**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### aspiration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "std      168\n",
       "turbo     37\n",
       "Name: aspiration, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['aspiration'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进气类型大部分是标准型，少量是涡轮增压"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### doornumber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    115\n",
       "two      90\n",
       "Name: doornumber, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['doornumber'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "四车门车的数量稍多于双车门车的数量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### carbody\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sedan          96\n",
       "hatchback      70\n",
       "wagon          25\n",
       "hardtop         8\n",
       "convertible     6\n",
       "Name: carbody, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['carbody'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='carbody', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='carbody', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**sedan厢式汽车和hatchback掀背式汽车占多数。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### drivewheel 驱动轮"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fwd    120\n",
       "rwd     76\n",
       "4wd      9\n",
       "Name: drivewheel, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drivewheel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37073170731707317"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "76/205"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**一半以上的汽车时前驱车，37%的车是后驱，少量车是四驱车**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### enginelocation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "front    202\n",
       "rear       3\n",
       "Name: enginelocation, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['enginelocation'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绝大部分车的引擎都在车头位置，只有三两车引擎的位置在车尾"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### enginetype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ohc      148\n",
       "ohcf      15\n",
       "ohcv      13\n",
       "l         12\n",
       "dohc      12\n",
       "rotor      4\n",
       "dohcv      1\n",
       "Name: enginetype, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['enginetype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='enginetype', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='enginetype', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分是顶置凸轮轴型的发动机"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cylindernumber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four      159\n",
       "six        24\n",
       "five       11\n",
       "eight       5\n",
       "two         4\n",
       "three       1\n",
       "twelve      1\n",
       "Name: cylindernumber, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cylindernumber'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cylindernumber', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='cylindernumber',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分车都是四缸车"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fuelsystem\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpfi    94\n",
       "2bbl    66\n",
       "idi     20\n",
       "1bbl    11\n",
       "spdi     9\n",
       "4bbl     3\n",
       "mfi      1\n",
       "spfi     1\n",
       "Name: fuelsystem, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fuelsystem'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fuelsystem', ylabel='count'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='fuelsystem', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "燃油系统最多的是mpfi多点燃油喷射系统，其次是二次空气喷射系统2bbl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = df.select_dtypes(exclude=\"object\").columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['car_ID', 'symboling', 'wheelbase', 'carlength', 'carwidth',\n",
       "       'carheight', 'curbweight', 'enginesize', 'boreratio', 'stroke',\n",
       "       'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',\n",
       "       'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### symboling\n",
    "保险安全等级，-3到3，数字越大越不安全，越小越危险\n",
    "\n",
    "symboling应该是类别数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    67\n",
       " 1    54\n",
       " 2    32\n",
       " 3    27\n",
       "-1    22\n",
       "-2     3\n",
       "Name: symboling, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['symboling'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='symboling', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='symboling', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绝大部分车属于危险等级较高的一部分，没有等级是-3的汽车"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### wheelbase\n",
    "轴距"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.5     21\n",
       "93.7     20\n",
       "95.7     13\n",
       "96.5      8\n",
       "98.4      7\n",
       "97.3      7\n",
       "104.3     6\n",
       "96.3      6\n",
       "98.8      6\n",
       "107.9     6\n",
       "100.4     6\n",
       "99.1      6\n",
       "93.1      5\n",
       "95.9      5\n",
       "102.4     5\n",
       "109.1     5\n",
       "97.2      5\n",
       "101.2     4\n",
       "97.0      4\n",
       "114.2     4\n",
       "95.3      4\n",
       "89.5      3\n",
       "105.8     3\n",
       "110.0     3\n",
       "103.5     3\n",
       "104.5     2\n",
       "103.3     2\n",
       "104.9     2\n",
       "102.9     2\n",
       "91.3      2\n",
       "115.6     2\n",
       "99.8      2\n",
       "113.0     2\n",
       "96.9      2\n",
       "86.6      2\n",
       "96.1      2\n",
       "88.6      2\n",
       "112.0     1\n",
       "93.3      1\n",
       "95.1      1\n",
       "102.7     1\n",
       "93.0      1\n",
       "102.0     1\n",
       "99.4      1\n",
       "99.5      1\n",
       "94.3      1\n",
       "99.2      1\n",
       "96.6      1\n",
       "106.7     1\n",
       "120.9     1\n",
       "88.4      1\n",
       "108.0     1\n",
       "96.0      1\n",
       "Name: wheelbase, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['wheelbase'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='wheelbase'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['wheelbase'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    205.000000\n",
       "mean      98.756585\n",
       "std        6.021776\n",
       "min       86.600000\n",
       "25%       94.500000\n",
       "50%       97.000000\n",
       "75%      102.400000\n",
       "max      120.900000\n",
       "Name: wheelbase, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['wheelbase'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### carlength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='carlength'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['carlength'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### carwidth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='carwidth'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['carwidth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### carheight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='carheight'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['carheight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### curbweight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='curbweight'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['curbweight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### enginesize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='enginesize'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['enginesize'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### boreratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='boreratio'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['boreratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stroke'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " ### stroke\n",
    "sns.distplot(df['stroke'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compressionratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='compressionratio'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['compressionratio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### horsepower\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='horsepower'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['horsepower'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### peakrpm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='peakrpm'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['peakrpm'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### citympg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='citympg'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['citympg'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### highwaympg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='highwaympg'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['highwaympg'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WKoziPRqRCrJSuXJpAT/dU6suKpEEpdCIY/XtfaQlB8hO83Vj/4y4Zc18jjR1sbemPdpNEZFpoNCIY/Ud4UHwaM+cinTLmnmkJAV4aseJ8XcWkbij0IhTzjka2vtipmtqWG5GMu9fVcLTu0/RFxqMdnNEZIopNOJUR2+InoHBmBkEj/SRioW0dg/wwn4tKyKSaBQacareWz4kVqbbRnrPskLm5qTx5HZ1UYkkGoVGnIqFhQrHEgwYH1pbyi8PNp6ZFiwiiUGhEaca2nvJTAmSlRo7M6ci3X7pAoYcPLXjZLSbIiJTSKERp+rbe2Oya2rYkqIsrlxSwPdeO8bA4FC0myMiU0ShEYecczR09MXkIHik331PGbVtvTyzty7aTRGRKaLQiEOn2nrpCw3F5HhGpOtXFrO4IIN/fflItJsiIlNEoRGHDtaHFwSM5iNe/QgEjN95dxm7TrSy45geBSuSCGJzFFXO6mBdODSi2T31g23Hfe035BxpyQH+fEsld61bdKb8rssXneUoEYlVutKIQwfrO8lOSyIjJfYzPzUpyLrFBVTWtFGn6bcicU+hEYcONXTE3PIhZ3N1eSEpSQGer9SAuEi8U2jEmaEhx6H6TopjfOZUpIzUJN67vIj9dR0ca+6KdnNEZBIUGnHmZEuPt+ZU/FxpALxraSFZqUk8W6nHwYrEM4VGnBmeOVWSHT9XGgApSQGuW1nMseZuKk/pWRsi8UqhEWeqhqfbxtmVBsBli/OZl5vGj3eforW7P9rNEZEJUGjEmUP1HczPTSMtORjtppyzYMD48NoFdPWHeOgn+6LdHBGZAIVGnDlY30l5SXa0mzFh8/PSee/yIv7fmzW8eKA+2s0RkXOk0Igjg0OO6sZOVsyN39AAuHZFMStKsvmjJ9/ixOnuaDdHRM6BQiOOHGvuoj80RHlxVrSbMilJwQDf+NhaQoNDfOrx7XT2haLdJBHxSaERRw7WdwKwPI67p4YtKcrikd9eS3VjJ1/ctJOQlk8XiQu+QsPM1ptZlZlVm9kDo7yfamZPeO9vM7PFEe896JVXmdmN49VpZmVeHdVenSkjPuvDZubMrGJCZxzHhqfblpfE95XGsKvKi/jzD67i5/sb+MKmXXruhkgcGDc0zCwIPALcBKwC7jSzVSN2uwdocc4tAx4GNnrHrgLuAFYD64FHzSw4Tp0bgYe9ulq8uofbkg18Adg2sdONbwfrO1iYnx4Xa0759YkrF/Nnt5zPT/fU8gfff5O+0GC0myQiZ+HnSmMdUO2cO+yc6wc2ARtG7LMBeNzbfgq43szMK9/knOtzzh0Bqr36Rq3TO+Y6rw68Om+L+Jy/Ihwqs3Llu0P1nSwvjv+uqZE+ddUSHtqwmuf31fOxb22jqbMv2k0SkTH4CY1S4ETE65Ne2aj7OOdCQBtQcJZjxyovAFq9Ot7xWWa2FljonPvp2RprZvea2XYz297Y2Ojj9OLDwOAQh5s6WR7nM6fG8okrF/OPd17Cnpo2bv2nl9lb0xbtJonIKOJiINzMAsDfA3843r7OuceccxXOuYqioqLpb9wMOdrUxcCgY3mCjGeM5taL5vPUZ94FwIe/8Rs2v3FinCNEZKb5CY0aYGHE6wVe2aj7mFkSkAs0n+XYscqbgTyvjsjybOAC4BdmdhS4AtgymwbDh2dOlSdg91SkC0pz2fK593DZ4nz+5Idv8UdP7qanX+McIrHCT2i8AZR7s5pSCA9sbxmxzxbgbm/7duBFF17KdAtwhze7qgwoB14fq07vmJe8OvDqfNo51+acK3TOLXbOLQZeA251zm2f4HnHnar6DgIGy+L8Hg0/CrNSefx31/H568v54Zsn+R+PvsLhxs5oN0tE8BEa3vjCZ4HngP3AZudcpZk9ZGa3ert9Gygws2rgfuAB79hKYDOwD3gWuM85NzhWnV5dXwLu9+oq8Oqe9Q7Vd3BeQWZcrjk1EcGAcf/7lvNvn7yM+vZebv3nV3h2rx7iJBJtlsjPNqioqHDbtyfGxch1X/sFy4qyeOwT4R45v8/ojlXn8ozwU609/MH332T3yVb+/AOr+OS7y6axZSJiZjucc6N2/yfOhP8E1hca5FhzN7esmRftpkTF/Lx0Nt17BZ//j538xY/3Udvey6I5GYRnaE/MuYSWiPyXuJg9NdsdbuxicMjF9eq2k5WWHOQbH7uUj12xiG/+8jDPVWqFXJFo0JVGHBhePmTFLA4NCI9z/NWGCwD43mvHSUsOcM2K4ii3SmR2UWjEgYP1HSQFjLLCzGg3ZcpMZkxm5dwcLlqQy8/21ZOVmkTF4vwpbJmInI1CIw4cqO1gaVEWKUnqTQQImHH7pQvp7h/k6d2nmJubxoI5GdFulsisoN9CceBAXQcr583urqmRggHjoxULyU5L4vvbjtOlZ3KIzAiFRoxr6xmgprWHlXNzot2UmJORmsRd6xbR1Rdi8/YTJPL0cZFYodCIcVV14UFwXWmMbsGcDG5eM49DDZ28cbQl2s0RSXgKjRh3oK4dgPN1pTGmy8vyWVqUyda9tbR090e7OSIJTaER4/bXdpCXkUxJTmq0mxKzzIwPXbIAHPxoZ426qUSmkUIjxh2oa2fl3OxJ3f08G8zJTGH9BXOpbuhk14nWaDdHJGEpNGLY0JCjqq5Dg+A+rSvLZ8GcdJ6trNNjY0WmiUIjhp1o6aa7f5DzNQjuS8CMD1w4n47eEL+oSpynNorEEoVGDNtf682c0pWGb4vyM7hkYR4vVzdxukuD4iJTTaERww7UtWMGy2f5mlPn6sbVcwma8cze2mg3RSThKDRi2IHaDsoKMklPmR0PXpoqOenJXLW8kMpT7Zxs6Y52c0QSikIjhu2va9dNfRP0nqWFZKYE+ZmWUBeZUgqNGNXeO8Cx5m5Wz8+NdlPiUmpykGtWFFPd2El1g54vLjJVFBoxam9NGwAXlCo0JmpdWT656cn8bF+dbvgTmSIKjRh1JjTma+bURCUHA1y/spiTLT0c8NbwEpHJUWjEqD017czPTaMgS8uHTMYli+aQn5nCC/vrdbUhMgUUGjFqb02buqamQDBgXLuimFNtvWfuexGRiVNoxKD23gGONHWxRqExJS5emBe+2jigqw2RyVJoxKDKmvBy6BcsUGhMhWDAuG5FMbVtveyvbY92c0TimkIjBg0PgutKY+pctDCPgswUXjjQoKsNkUlQaMSgPTVtzMtNo1CD4FMmGDCuXRm+2tinqw2RCVNoxCANgk+PixaErzZePNDA0JCuNkQmQqERYzp6BzisQfBpEQwY13lXGz/bVxft5ojEJYVGjKk8Fe46UWhMjwsX5FGYlcI//PyQrjZEJkChEWN2Hm8F4ELNnJoWw/dtHKjr4LlKXW2InCuFRozZcew0SwozdSf4NLpoYR5LCjP5+gu62hA5VwqNGOKcY8exFi49b060m5LQAmZ8/vpyDtR18KyuNkTOia/QMLP1ZlZlZtVm9sAo76ea2RPe+9vMbHHEew965VVmduN4dZpZmVdHtVdnild+v5ntM7O3zOwFMztvUmcegw43ddHSPaDQmAEfvGg+S4sy+brGNkTOybihYWZB4BHgJmAVcKeZrRqx2z1Ai3NuGfAwsNE7dhVwB7AaWA88ambBcercCDzs1dXi1Q2wE6hwzl0IPAX87cROOXbtONYCoNCYAcFA+Gqjqr6DZ/bqakPELz9XGuuAaufcYedcP7AJ2DBinw3A4972U8D1ZmZe+SbnXJ9z7ghQ7dU3ap3eMdd5deDVeRuAc+4l59zwsztfAxac89nGuDePtZCbnszSoqxoN2VW+MCF81lWnMXXXzioqw0Rn/yERilwIuL1Sa9s1H2ccyGgDSg4y7FjlRcArV4dY30WhK8+nhmtsWZ2r5ltN7PtjY2N455cLNl+rIW1i/IIBCzaTZkVhq82DtZ3snVvbbSbIxIX4m4g3Mw+BlQAfzfa+865x5xzFc65iqKioplt3CS0dvdT3dBJxeL8aDdlVrllzbzw1YbGNkR88RMaNcDCiNcLvLJR9zGzJCAXaD7LsWOVNwN5Xh3/7bPM7Abgy8Ctzrk+H22PG28eD49nrF2k8YyZFAwYX7i+nEMNnfx0j642RMbjJzTeAMq9WU0phAe2t4zYZwtwt7d9O/CiCy8lugW4w5tdVQaUA6+PVad3zEteHXh1Pg1gZpcA3yQcGA0TO93YteNYC8GAcdFC3dQ3025eM4/lJVn8/fMH6Q8NRbs5IjFt3NDwxhc+CzwH7Ac2O+cqzewhM7vV2+3bQIGZVQP3Aw94x1YCm4F9wLPAfc65wbHq9Or6EnC/V1eBVzeEu6OygCfNbJeZjQyuuPb6kdOsnp9DRkrS+DvLlAoGjAdvPp8jTV1877Vj0W6OSEzz9RvKObcV2Dqi7CsR273AR8Y49qvAV/3U6ZUfJjy7amT5DX7aGo86+0LsPN7Kp69eEu2mzFrXLC/iqvJCvv7CIT60tpS8jJRoN0kkJsXdQHgieu3tZkJDjqvKC6PdlFnLzPjyLefT0TvA1184FO3miMQshUYM+PWhRtKTg7qpL8pWzs3ho5ct4ruvHuNgfUe0myMSkxQaMeDX1U2sK8snNSkY7abMen/0/uVkpSXx5R/t0RRckVEoNKLsVGsPhxu71DUVIwqyUvnTm87njaMtbN5+YvwDRGYZhUaUvXyoCYCryuPnRsRE95GKBawry+f/PHOAps6Euh1IZNIUGlH26+omirNTWV6i9aZihZnxv//HBXT3h/izH+0lfPuQiIBCI6qGhhyvVDfxnmWFhNdqlFixrDibP3r/Cp6trOPJHSej3RyRmKHQiKKdJ1o53dXP1cvVNRWLPnXVEq5Yks9fbqnkWHNXtJsjEhMUGlG0dU8tKcEA151fHO2myCiCAeNrv3UxgYDx+U276AsNRrtJIlGn0IiSoSHHM3tquXp5ITlpydFujoyhNC+dv7v9QnafaOUr/1mp8Q2Z9RQaUbLrZCun2nq5ec28aDdFxrH+gnl89tplPLH9hNamkllPoRElW9+qJTlo3LCqJNpNER/uf99yrl9ZzF/+eB+/PBhfD/cSmUoKjShwzvHM3jquKi9S11ScCASMh++4mPKSbD7z3R3sOHY62k0SiQqFRhTsOtFKTWuPuqbiTE5aMt/53XXMzU3jk//2BpWn2qLdJJEZp9CIgqd3nSIlGOB96pqKO0XZqXzvU5eTlZrEnY+9pisOmXUUGjOsqy/ED3ec5OY1c8lNV9dUPCrNS2fz711JfmYKv/2tbbxUlXAPkhQZk0Jjhv1oZw0dfSE+fuXiaDdFJmFhfgZPfuZdLC3K4lOPb+fbLx/RdFyZFRQaM8g5x3dePcrq+TmsXZQX7ebIJBVlp7Lp3iu4fmUxf/WTfXzxiV1094ei3SyRaaUHUs+gbUdOc7C+k40fXqO1pqLsB9uOT+r4uy5fBEB2WjL/8rFLefQX1Xzt+YO8dbKNr/3WRaxdlPgP1Jqqv0OJLwqNGfTdV4+Rm57MrReVRrspMoUCAeOz15Wz9rw5/PGTb3H7N37Dp69ewheuLycjJb7/F3PO0d4TorGzl/beEN19g/QPDmJmHGroIC0pSHpykKy0JNKS9RCx2SC+v9Fx5EBdO1v31vJ7Vy8lPUX/cyWidy0t5NkvXsVf/2Q/3/zlYbbsOsWf3nw+H7hwXlxcWda397L7RCt7atqoquvgaHMXx0930zsw5Ov49OQgBVkpzM1JY15uGgvzM5iXm04wEPvnLv4pNGbIxmcOkJ2axGfeuyTaTZEpcLaumYsW5pGXkcyW3af43H/s5G+eOcAN5xezvCT7THhEu2tmaMjx988f5O3GTo42d1PT0k17b3g8JmBQkJlKYVYKly6aQ056MtlpyaQnB0lJCpAUMJxXR29okJ7+Qdp7Q7R099Pc2ce+2na2H2sBIDUpwOKCTFbNy2HlvGyydTNr3FNozIDfVDfxUlUjf3rzSvIyUqLdHJkB5xVkct+1y9h5vJUXD9Tz+KvHmJ+XxruWFnJhae6Mt8c5x/HT3bxS3cwrbzfx6tvNnO7qB6AgM4WywkwWzMlgwZx05uWmk5I08TkyzjnaegY4drqbI01dHKrvoKq+A9sVnnW2al4Oq+fnTNGZyUyzRJ4mWFFR4bZv3x7VNgwNOTY88gqnu/p54Q/fO2X9vpMdhJSZMzjkePN4Cy9XN9HY0UdmSpCPVCzkQ2tLWVOaO21dV6dae3jtcDOvHW7mlepmalp7ACjJSeXdywoJmLG0KGva7xdyzlHX3su+2nb2n2rnVFsvABcvzOO2i+fzgYvmU5iVOq1tkHNjZjuccxWjvqfQmF7fffUo/+vpSr72kYv48KULpqxehUb8cc5R3djJ60dOc6i+k/7BIUrz0rlmRRFXLy9i7aI5FGX7/+UZ+R3oCw1S39ZLbXsvJ1t6ONLUdeZKIj05SFlhJkuLs1halElRVmpUx1hauvp5q6aNY81dHKjrIBgw3rOskNsumc/7V80lM1UdINGm0IiS/bXtbHjkFa5cUsC/ffIyAlM4IKjQiG+3rJnHs5W1vLC/gZerm+juDz/gqTQvnfKSLJYUZjE/L405GSnkpieTFDSCAaOnf5Cu/hCnuwZ46UADLd391LX1crqrn+H/k4dDoqwwkyVFmZTkpBGIwYH4uy5fRFVdB/+5q4Ytu05R09pDenKQ960qYcPF87l6eRHJQd1KFg0KjSjo7g/xwX96mY7eEFu/cNWUX34rNOJb5EB4X2iQvTVtvHmsld0nWznc2MWRpi56Bs7+pMCkgJGbnkyJN1tpXm4683LTyMtIjovZWpF/B0NDju3HWvjPXTVs3VNLa/cAczKSuWZFMdesKOKq8iLyMzUeOFPOFhq6DpwGocEh/vjJtzjc1MX377lc/bVyVqlJQS49L59Lz8s/U+aco703xOmuftp7Bhh0jsEhR3pykIyUIHMyUti6pzYuwsGPQMBYV5bPurJ8/uKDq/nVwUZ+8tYpflHVwI921mAGFy7I4+ryQi5emMeaBbkUZ6dFu9mzkkJjig0MDvGFTTvZuqeOL998Pu9aVhjtJkkcMgtfRZxtkDreA2O8q+V1ZQVULM6npqWHgw0dHKrv5J9frD7TDZeTlkTpnAzm5aZRmJVCQWYqBZkpZPgcE4n2tOd4pdCYQh29A9y/eTfP76vnz245n09dpXsyRCYjYMbC/AwW5mdw/coS+kKD1Lb2UtPaE/5p6eFAbTuRnezpyUHmZCST44VubnrEdlp4ezJTimc7hcYU+c3bTfzxk29R29bDX966mrvftTjaTRJJOKlJQRYXZrK4MPNMWWhwiNPd/TR39tPcFb7BsLV7gLaeAY6f7j4zySBSenKQx39zlLm54fGg4pw0irNTKcpOPfNnYVaqlkYZhUJjkvbWtPHNXx3mx7tPUVaYyZOfeReXnpf4i9XJ5Ggiw9RJCgYozk4bc4xjYHCI9p5wiLT1DIS3ewdo6wlR3dDJjmMtdPWFGG1K0PC6WtlpSWSnJpGdlkx2WhJZqUncdknpmXDJS0+e0tmRscxXaJjZeuDrQBD4lnPub0a8nwp8B7gUaAY+6pw76r33IHAPMAh83jn33NnqNLMyYBNQAOwAPu6c6z/bZ8wk5xxHm7t5YX89P9tXz+tHTpOVmsTvX7OUz123LO4XqBNJNMnBAAVZqRScZULK4JCjqy9ER1+Ijt4BOntDtPeG6OwboKM3REdviOOnu+noDREaCsfLkztOnjk+GDDyM1MozAovvxL5Z8E7ylIpyEqJ66nE4/6GM7Mg8AjwPuAk8IaZbXHO7YvY7R6gxTm3zMzuADYCHzWzVcAdwGpgPvBzM1vuHTNWnRuBh51zm8zsX7y6vzHWZ0z2L2A0LV39nGzpoa1n4Mw8+JrWHt5u7GRvTRst3QMArCjJ5k/Wr+C3Lz9PT+ETiWPBgJHjjX1A+pj7OefoCw3R0RtiXVk+jZ19NHf20dTZR1NHP81dfTR29nO4sYumzj76QqMv9piXkRwOkMwUCrNTKcpKJT8zhazUJDJSgmSkJpGZEiQjJfw6MzVIcjBAwOzMPTtJgYD3p7cWmHMMDYVn2Q05yEpNmpbFUf38s3gdUO2cOwxgZpuADUBkaGwA/sLbfgr4ZwtP7dgAbHLO9QFHzKzaq4/R6jSz/cB1wF3ePo979X5jrM9w03CjyaY3TrDx2QPvKMtKTWJxYQbvXzWXC0pzeO/yYhYVZEz1R4tIDDMz0pKDpCUHuXJpwVn3dc7R1T9IU0dfOEw6+sPh0tlHc+d/be8/1c6vOvvo6J3aB3j99W0X8LErzpvSOsFfaJQCJyJenwQuH2sf51zIzNoIdy+VAq+NOHb4YRKj1VkAtDrnQqPsP9ZnNEU2xMzuBe71XnaaWZWPc/SlEvjp6G8VjmxHgtH5xTed3zT47Zn7qAmd38c3wscn/pljpk3CdcA75x4DHpvJzzSz7WPdPZkIdH7xTecX32Lt/PyMxtQACyNeL/DKRt3HzJKAXMKD1WMdO1Z5M5Dn1THys8b6DBERmSF+QuMNoNzMyswshfDA9pYR+2wB7va2bwde9MYatgB3mFmqNyuqHHh9rDq9Y17y6sCr8+lxPkNERGbIuN1T3vjBZ4HnCE+P/VfnXKWZPQRsd85tAb4NfNcb6D5NOATw9ttMeNA8BNznnBsEGK1O7yO/BGwys78Gdnp1M9ZnxIgZ7Q6LAp1ffNP5xbeYOr+EXuVWRESmVvzeYSIiIjNOoSEiIr4pNCbJzNabWZWZVZvZA9Fuz9mY2b+aWYOZ7Y0oyzez583skPfnHK/czOwfvfN6y8zWRhxzt7f/ITO7O6L8UjPb4x3zjzaDa3eb2UIze8nM9plZpZl9IcHOL83MXjez3d75/aVXXmZm27w2PeFNLMGbfPKEV77NzBZH1PWgV15lZjdGlEf9u2xmQTPbaWY/8V4nzPmZ2VHv+7PLzLZ7ZfH3/XTO6WeCP4QH8d8GlgApwG5gVbTbdZb2Xg2sBfZGlP0t8IC3/QCw0du+GXgGMOAKYJtXng8c9v6c423P8d573dvXvGNvmsFzmwes9bazgYPAqgQ6PwOyvO1kYJvXls3AHV75vwC/723/AfAv3vYdwBPe9irve5oKlHnf32CsfJeB+4EfAD/xXifM+QFHgcIRZXH3/dSVxuScWWLFOddPeKHFDVFu05icc78iPPMs0gbCy7Xg/XlbRPl3XNhrhO+fmQfcCDzvnDvtnGsBngfWe+/lOOdec+Fv8Hci6pp2zrla59yb3nYHsJ/wKgKJcn7OOdfpvUz2fhzhZXee8spHnt/weT8FXO/9y/PM0j7OuSPA8NI+Uf8um9kC4BbgW95rI4HObwxx9/1UaEzOaEuslI6xb6wqcc7Vett1QIm3Pda5na385CjlM87rqriE8L/GE+b8vK6bXUAD4V8Wb+Nz2R0gcmmfcznvmfQPwJ8Aw6v8+V5WiPg4Pwf8zMx2WHi5I4jD72fCLSMiE+ecc2YW13OwzSwL+CHwRedce2S3bryfnwvf43SxmeUBPwJWRrdFU8fMPgA0OOd2mNk1UW7OdHmPc67GzIqB583sHauixsv3U1cak+NniZVYV+9d2uL92eCVn+sSMDXe9sjyGWNmyYQD4/vOuf/nFSfM+Q1zzrUSXjnhSs592Z1zPe+Z8m7gVjM7Srjr6DrCz9tJlPPDOVfj/dlAOPTXEY/fz5kcCEq0H8JXaocJD7gND66tjna7xmnzYt45EP53vHMg7m+97Vt450Dc6155PnCE8CDcHG8733tv5EDczTN4Xka4H/cfRpQnyvkVAXnedjrwa+ADwJO8c6D4D7zt+3jnQPFmb3s17xwoPkx4kDhmvsvANfzXQHhCnB+QCWRHbP8GWB+P388Z/0Ik2g/hWQ4HCfcvfzna7Rmnrf8B1AIDhPs87yHcD/wCcAj4ecQX0Ag/KOttYA9QEVHP7xIeYKwGfieivALY6x3zz3grDszQub2HcJ/xW8Au7+fmBDq/Cwkvq/OW14aveOVLvF8W1YR/waZ65Wne62rv/SURdX3ZO4cqImbYxMp3mXeGRkKcn3ceu72fyuHPj8fvp5YRERER3zSmISIivik0RETEN4WGiIj4ptAQERHfFBoiIuKbQkNkhpnZQ2Z2Q7TbITIRmnIrMoPMLOi8Rx6LxCNdaYhMETNbbGYHzOz7ZrbfzJ4yswzvOQobzexN4CNm9u9mdrt3zGVm9hvvORmvm1m2tzDh35nZG96zFH4vyqcmcoZCQ2RqrQAedc6dD7QTfu4DQLNzbq1zbtPwjt4DhZ4AvuCcuwi4AeghfKd+m3PuMuAy4NNmVjaTJyEyFoWGyNQ64Zx7xdv+HuHlTSAcDiOtAGqdc28AOOfaXXiZ7/cDn/CWQd9GeKmJ8mlttYhPWhpdZGqNHCQcft11DnUY8Dnn3HNT0ySRqaMrDZGptcjMrvS27wJePsu+VcA8M7sMwBvPSAKeA37fW+odM1tuZpnT2WgRvxQaIlOrCrjPzPYTXrr6G2Pt6MKPHf0o8E9mtpvw0/jSCD/udB/wppntBb6JegUkRmjKrcgU8R4z+xPn3AXRbovIdNGVhoiI+KYrDRER8U1XGiIi4ptCQ0REfFNoiIiIbwoNERHxTaEhIiK+/X/rxHcONXdEaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程\n",
    "类别特征数值化，数值特征标准化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 类别特征数值化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['car_ID', 'symboling', 'CarName', 'fueltype', 'aspiration',\n",
       "       'doornumber', 'carbody', 'drivewheel', 'enginelocation', 'wheelbase',\n",
       "       'carlength', 'carwidth', 'carheight', 'curbweight', 'enginetype',\n",
       "       'cylindernumber', 'enginesize', 'fuelsystem', 'boreratio', 'stroke',\n",
       "       'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',\n",
       "       'price', 'brand'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = df.select_dtypes(include='object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CarName', 'fueltype', 'aspiration', 'doornumber', 'carbody',\n",
       "       'drivewheel', 'enginelocation', 'enginetype', 'cylindernumber',\n",
       "       'fuelsystem', 'brand'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = list(cat_cols)\n",
    "cat_cols.remove('CarName')  # CarName 已经用brand替代\n",
    "df.drop('CarName', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_cols:\n",
    "    df[col] = LabelEncoder().fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# symboling改为object类型\n",
    "df['symboling'] = df['symboling'].astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['symboling'] = LabelEncoder().fit_transform(df['symboling'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>brand</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>201</th>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     fueltype  aspiration  doornumber  carbody  drivewheel  enginelocation  \\\n",
       "0           1           0           1        0           2               0   \n",
       "1           1           0           1        0           2               0   \n",
       "2           1           0           1        2           2               0   \n",
       "3           1           0           0        3           1               0   \n",
       "4           1           0           0        3           0               0   \n",
       "..        ...         ...         ...      ...         ...             ...   \n",
       "200         1           0           0        3           2               0   \n",
       "201         1           1           0        3           2               0   \n",
       "202         1           0           0        3           2               0   \n",
       "203         0           1           0        3           2               0   \n",
       "204         1           1           0        3           2               0   \n",
       "\n",
       "     enginetype  cylindernumber  fuelsystem  brand  symboling  \n",
       "0             0               2           5      0          5  \n",
       "1             0               2           5      0          5  \n",
       "2             5               3           5      0          3  \n",
       "3             3               2           5      1          4  \n",
       "4             3               1           5      1          4  \n",
       "..          ...             ...         ...    ...        ...  \n",
       "200           3               2           5     21          1  \n",
       "201           3               2           5     21          1  \n",
       "202           5               3           5     21          1  \n",
       "203           3               3           3     21          1  \n",
       "204           3               2           5     21          1  \n",
       "\n",
       "[205 rows x 11 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols.append('symboling')\n",
    "df[cat_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值特征标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['car_ID',\n",
       " 'symboling',\n",
       " 'wheelbase',\n",
       " 'carlength',\n",
       " 'carwidth',\n",
       " 'carheight',\n",
       " 'curbweight',\n",
       " 'enginesize',\n",
       " 'boreratio',\n",
       " 'stroke',\n",
       " 'compressionratio',\n",
       " 'horsepower',\n",
       " 'peakrpm',\n",
       " 'citympg',\n",
       " 'highwaympg',\n",
       " 'price']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cols = list(get_data().select_dtypes(exclude='object').columns)\n",
    "numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols.remove('car_ID')\n",
    "numeric_cols.remove('symboling')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "for col in numeric_cols:\n",
    "    df[col] = MinMaxScaler().fit_transform(df[[col]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th>carlength</th>\n",
       "      <th>...</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
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       "      <th>peakrpm</th>\n",
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       "      <th>highwaympg</th>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.12500</td>\n",
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       "      <td>0.282558</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.633333</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>0.279167</td>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.306142</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.291123</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.655977</td>\n",
       "      <td>0.711940</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.10625</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.345738</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.655977</td>\n",
       "      <td>0.711940</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.11250</td>\n",
       "      <td>0.358333</td>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.184211</td>\n",
       "      <td>0.406311</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.655977</td>\n",
       "      <td>0.711940</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.335714</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.241667</td>\n",
       "      <td>0.265306</td>\n",
       "      <td>0.361111</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.430763</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.655977</td>\n",
       "      <td>0.711940</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.434611</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     car_ID  symboling  fueltype  aspiration  doornumber  carbody  drivewheel  \\\n",
       "0         1          5         1           0           1        0           2   \n",
       "1         2          5         1           0           1        0           2   \n",
       "2         3          3         1           0           1        2           2   \n",
       "3         4          4         1           0           0        3           1   \n",
       "4         5          4         1           0           0        3           0   \n",
       "..      ...        ...       ...         ...         ...      ...         ...   \n",
       "200     201          1         1           0           0        3           2   \n",
       "201     202          1         1           1           0        3           2   \n",
       "202     203          1         1           0           0        3           2   \n",
       "203     204          1         0           1           0        3           2   \n",
       "204     205          1         1           1           0        3           2   \n",
       "\n",
       "     enginelocation  wheelbase  carlength  ...  fuelsystem  boreratio  \\\n",
       "0                 0   0.058309   0.413433  ...           5   0.664286   \n",
       "1                 0   0.058309   0.413433  ...           5   0.664286   \n",
       "2                 0   0.230321   0.449254  ...           5   0.100000   \n",
       "3                 0   0.384840   0.529851  ...           5   0.464286   \n",
       "4                 0   0.373178   0.529851  ...           5   0.464286   \n",
       "..              ...        ...        ...  ...         ...        ...   \n",
       "200               0   0.655977   0.711940  ...           5   0.885714   \n",
       "201               0   0.655977   0.711940  ...           5   0.885714   \n",
       "202               0   0.655977   0.711940  ...           5   0.742857   \n",
       "203               0   0.655977   0.711940  ...           3   0.335714   \n",
       "204               0   0.655977   0.711940  ...           5   0.885714   \n",
       "\n",
       "       stroke  compressionratio  horsepower   peakrpm   citympg  highwaympg  \\\n",
       "0    0.290476           0.12500    0.262500  0.346939  0.222222    0.289474   \n",
       "1    0.290476           0.12500    0.262500  0.346939  0.222222    0.289474   \n",
       "2    0.666667           0.12500    0.441667  0.346939  0.166667    0.263158   \n",
       "3    0.633333           0.18750    0.225000  0.551020  0.305556    0.368421   \n",
       "4    0.633333           0.06250    0.279167  0.551020  0.138889    0.157895   \n",
       "..        ...               ...         ...       ...       ...         ...   \n",
       "200  0.514286           0.15625    0.275000  0.510204  0.277778    0.315789   \n",
       "201  0.514286           0.10625    0.466667  0.469388  0.166667    0.236842   \n",
       "202  0.380952           0.11250    0.358333  0.551020  0.138889    0.184211   \n",
       "203  0.633333           1.00000    0.241667  0.265306  0.361111    0.289474   \n",
       "204  0.514286           0.15625    0.275000  0.510204  0.166667    0.236842   \n",
       "\n",
       "        price  brand  \n",
       "0    0.207959      0  \n",
       "1    0.282558      0  \n",
       "2    0.282558      0  \n",
       "3    0.219254      1  \n",
       "4    0.306142      1  \n",
       "..        ...    ...  \n",
       "200  0.291123     21  \n",
       "201  0.345738     21  \n",
       "202  0.406311     21  \n",
       "203  0.430763     21  \n",
       "204  0.434611     21  \n",
       "\n",
       "[205 rows x 26 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.354419</td>\n",
       "      <td>0.491780</td>\n",
       "      <td>0.467317</td>\n",
       "      <td>0.493740</td>\n",
       "      <td>0.414106</td>\n",
       "      <td>0.248707</td>\n",
       "      <td>0.564111</td>\n",
       "      <td>0.564483</td>\n",
       "      <td>0.196409</td>\n",
       "      <td>0.233821</td>\n",
       "      <td>0.398009</td>\n",
       "      <td>0.339431</td>\n",
       "      <td>0.388190</td>\n",
       "      <td>0.202540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.175562</td>\n",
       "      <td>0.184139</td>\n",
       "      <td>0.178767</td>\n",
       "      <td>0.203627</td>\n",
       "      <td>0.201971</td>\n",
       "      <td>0.157142</td>\n",
       "      <td>0.193460</td>\n",
       "      <td>0.149332</td>\n",
       "      <td>0.248253</td>\n",
       "      <td>0.164767</td>\n",
       "      <td>0.194688</td>\n",
       "      <td>0.181726</td>\n",
       "      <td>0.181222</td>\n",
       "      <td>0.198323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.230321</td>\n",
       "      <td>0.376119</td>\n",
       "      <td>0.316667</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.254849</td>\n",
       "      <td>0.135849</td>\n",
       "      <td>0.435714</td>\n",
       "      <td>0.495238</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.091667</td>\n",
       "      <td>0.265306</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.066283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.303207</td>\n",
       "      <td>0.479104</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.359193</td>\n",
       "      <td>0.222642</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.580952</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.195833</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.128519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.460641</td>\n",
       "      <td>0.626866</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.641667</td>\n",
       "      <td>0.561288</td>\n",
       "      <td>0.301887</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.638095</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.283333</td>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.282632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        wheelbase   carlength    carwidth   carheight  curbweight  enginesize  \\\n",
       "count  205.000000  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
       "mean     0.354419    0.491780    0.467317    0.493740    0.414106    0.248707   \n",
       "std      0.175562    0.184139    0.178767    0.203627    0.201971    0.157142   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.230321    0.376119    0.316667    0.350000    0.254849    0.135849   \n",
       "50%      0.303207    0.479104    0.433333    0.525000    0.359193    0.222642   \n",
       "75%      0.460641    0.626866    0.550000    0.641667    0.561288    0.301887   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "        boreratio      stroke  compressionratio  horsepower     peakrpm  \\\n",
       "count  205.000000  205.000000        205.000000  205.000000  205.000000   \n",
       "mean     0.564111    0.564483          0.196409    0.233821    0.398009   \n",
       "std      0.193460    0.149332          0.248253    0.164767    0.194688   \n",
       "min      0.000000    0.000000          0.000000    0.000000    0.000000   \n",
       "25%      0.435714    0.495238          0.100000    0.091667    0.265306   \n",
       "50%      0.550000    0.580952          0.125000    0.195833    0.428571   \n",
       "75%      0.742857    0.638095          0.150000    0.283333    0.551020   \n",
       "max      1.000000    1.000000          1.000000    1.000000    1.000000   \n",
       "\n",
       "          citympg  highwaympg       price  \n",
       "count  205.000000  205.000000  205.000000  \n",
       "mean     0.339431    0.388190    0.202540  \n",
       "std      0.181726    0.181222    0.198323  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.166667    0.236842    0.066283  \n",
       "50%      0.305556    0.368421    0.128519  \n",
       "75%      0.472222    0.473684    0.282632  \n",
       "max      1.000000    1.000000    1.000000  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 建模\n",
    "采用Kmeans聚类\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = df.drop('car_ID', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1040: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  \"KMeans is known to have a memory leak on Windows \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29c9c078d08>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = []\n",
    "for k in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=k,random_state=6)\n",
    "    kmeans.fit(train_data)\n",
    "    loss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,10), loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1,\n",
       "       1, 5, 1, 1, 1, 5, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1,\n",
       "       1, 1, 5, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 4,\n",
       "       2, 5, 5, 5, 5, 5, 5, 5, 5, 4, 2, 2, 2, 4, 4, 2, 4, 4, 4, 2, 2, 4,\n",
       "       4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 2, 4,\n",
       "       2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3,\n",
       "       3, 3, 3, 3, 3, 3, 0, 3, 0, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# k取6\n",
    "model = KMeans(n_clusters=6)\n",
    "model.fit(train_data)\n",
    "car_class = model.predict(train_data)\n",
    "car_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['class'] = car_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['brand'] = data['CarName'].apply(lambda x: x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = data[['car_ID', 'brand', 'CarName', 'class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>brand</th>\n",
       "      <th>CarName</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>audi</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>audi</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>201</td>\n",
       "      <td>volvo</td>\n",
       "      <td>volvo 145e (sw)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>volvo</td>\n",
       "      <td>volvo 144ea</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>203</td>\n",
       "      <td>volvo</td>\n",
       "      <td>volvo 244dl</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>204</td>\n",
       "      <td>volvo</td>\n",
       "      <td>volvo 246</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>volvo</td>\n",
       "      <td>volvo 264gl</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     car_ID        brand                   CarName  class\n",
       "0         1  alfa-romero        alfa-romero giulia      5\n",
       "1         2  alfa-romero       alfa-romero stelvio      5\n",
       "2         3  alfa-romero  alfa-romero Quadrifoglio      5\n",
       "3         4         audi               audi 100 ls      5\n",
       "4         5         audi                audi 100ls      5\n",
       "..      ...          ...                       ...    ...\n",
       "200     201        volvo           volvo 145e (sw)      3\n",
       "201     202        volvo               volvo 144ea      3\n",
       "202     203        volvo               volvo 244dl      3\n",
       "203     204        volvo                 volvo 246      3\n",
       "204     205        volvo               volvo 264gl      3\n",
       "\n",
       "[205 rows x 4 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>brand</th>\n",
       "      <th>CarName</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>151</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corona mark ii</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corona</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla 1200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corona hardtop</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla 1600 (sw)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota carina</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>157</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota mark ii</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>158</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla 1200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>159</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corona</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>160</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>161</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corona</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>162</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>163</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota mark ii</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>164</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla liftback</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>165</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corona</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>166</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota celica gt liftback</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>167</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla tercel</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>168</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corona liftback</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>169</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>170</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota starlet</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>171</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota tercel</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>172</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>173</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota cressida</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>174</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>175</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota celica gt</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>176</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corona</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>177</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>178</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota mark ii</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>179</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corolla liftback</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>180</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota corona</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>181</td>\n",
       "      <td>toyota</td>\n",
       "      <td>toyota starlet</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     car_ID   brand                    CarName  class\n",
       "150     151  toyota      toyota corona mark ii      0\n",
       "151     152  toyota              toyota corona      0\n",
       "152     153  toyota        toyota corolla 1200      0\n",
       "153     154  toyota      toyota corona hardtop      0\n",
       "154     155  toyota   toyota corolla 1600 (sw)      0\n",
       "155     156  toyota              toyota carina      0\n",
       "156     157  toyota             toyota mark ii      0\n",
       "157     158  toyota        toyota corolla 1200      0\n",
       "158     159  toyota              toyota corona      0\n",
       "159     160  toyota             toyota corolla      0\n",
       "160     161  toyota              toyota corona      0\n",
       "161     162  toyota             toyota corolla      0\n",
       "162     163  toyota             toyota mark ii      0\n",
       "163     164  toyota    toyota corolla liftback      0\n",
       "164     165  toyota              toyota corona      0\n",
       "165     166  toyota  toyota celica gt liftback      3\n",
       "166     167  toyota      toyota corolla tercel      3\n",
       "167     168  toyota     toyota corona liftback      3\n",
       "168     169  toyota             toyota corolla      3\n",
       "169     170  toyota             toyota starlet      3\n",
       "170     171  toyota              toyota tercel      3\n",
       "171     172  toyota             toyota corolla      3\n",
       "172     173  toyota            toyota cressida      3\n",
       "173     174  toyota             toyota corolla      3\n",
       "174     175  toyota           toyota celica gt      0\n",
       "175     176  toyota              toyota corona      3\n",
       "176     177  toyota             toyota corolla      3\n",
       "177     178  toyota             toyota mark ii      3\n",
       "178     179  toyota    toyota corolla liftback      3\n",
       "179     180  toyota              toyota corona      3\n",
       "180     181  toyota             toyota starlet      3"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以toyota 丰田为例输出其竞品车型\n",
    "toyota = result[result['brand']=='toyota']\n",
    "toyota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'toyota corona mark ii': 0,\n",
       " 'toyota corona': 3,\n",
       " 'toyota corolla 1200': 0,\n",
       " 'toyota corona hardtop': 0,\n",
       " 'toyota corolla 1600 (sw)': 0,\n",
       " 'toyota carina': 0,\n",
       " 'toyota mark ii': 3,\n",
       " 'toyota corolla': 3,\n",
       " 'toyota corolla liftback': 3,\n",
       " 'toyota celica gt liftback': 3,\n",
       " 'toyota corolla tercel': 3,\n",
       " 'toyota corona liftback': 3,\n",
       " 'toyota starlet': 3,\n",
       " 'toyota tercel': 3,\n",
       " 'toyota cressida': 3,\n",
       " 'toyota celica gt': 0}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toyota_dict = {x[-2]:x[-1] for x in toyota.values}\n",
    "toyota_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "toyota corona mark ii 的竞品车型是:\n",
      "['subaru', 'subaru dl', 'subaru brz', 'subaru r1', 'subaru trezia', 'toyota corona mark ii', 'toyota corona', 'toyota corolla 1200', 'toyota corona hardtop', 'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii', 'toyota corolla', 'toyota corolla liftback', 'toyota celica gt', 'vokswagen rabbit', 'volkswagen model 111', 'volkswagen super beetle', 'volkswagen rabbit custom']\n",
      "\n",
      "toyota corona 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota corolla 1200 的竞品车型是:\n",
      "['subaru', 'subaru dl', 'subaru brz', 'subaru r1', 'subaru trezia', 'toyota corona mark ii', 'toyota corona', 'toyota corolla 1200', 'toyota corona hardtop', 'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii', 'toyota corolla', 'toyota corolla liftback', 'toyota celica gt', 'vokswagen rabbit', 'volkswagen model 111', 'volkswagen super beetle', 'volkswagen rabbit custom']\n",
      "\n",
      "toyota corona hardtop 的竞品车型是:\n",
      "['subaru', 'subaru dl', 'subaru brz', 'subaru r1', 'subaru trezia', 'toyota corona mark ii', 'toyota corona', 'toyota corolla 1200', 'toyota corona hardtop', 'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii', 'toyota corolla', 'toyota corolla liftback', 'toyota celica gt', 'vokswagen rabbit', 'volkswagen model 111', 'volkswagen super beetle', 'volkswagen rabbit custom']\n",
      "\n",
      "toyota corolla 1600 (sw) 的竞品车型是:\n",
      "['subaru', 'subaru dl', 'subaru brz', 'subaru r1', 'subaru trezia', 'toyota corona mark ii', 'toyota corona', 'toyota corolla 1200', 'toyota corona hardtop', 'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii', 'toyota corolla', 'toyota corolla liftback', 'toyota celica gt', 'vokswagen rabbit', 'volkswagen model 111', 'volkswagen super beetle', 'volkswagen rabbit custom']\n",
      "\n",
      "toyota carina 的竞品车型是:\n",
      "['subaru', 'subaru dl', 'subaru brz', 'subaru r1', 'subaru trezia', 'toyota corona mark ii', 'toyota corona', 'toyota corolla 1200', 'toyota corona hardtop', 'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii', 'toyota corolla', 'toyota corolla liftback', 'toyota celica gt', 'vokswagen rabbit', 'volkswagen model 111', 'volkswagen super beetle', 'volkswagen rabbit custom']\n",
      "\n",
      "toyota mark ii 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota corolla 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota corolla liftback 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota celica gt liftback 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota corolla tercel 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota corona liftback 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota starlet 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota tercel 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota cressida 的竞品车型是:\n",
      "['renault 12tl', 'renault 5 gtl', 'saab 99e', 'saab 99le', 'saab 99gle', 'subaru baja', 'subaru r2', 'subaru tribeca', 'subaru dl', 'toyota celica gt liftback', 'toyota corolla tercel', 'toyota corona liftback', 'toyota corolla', 'toyota starlet', 'toyota tercel', 'toyota cressida', 'toyota corona', 'toyota mark ii', 'toyota corolla liftback', 'toyouta tercel', 'volkswagen 1131 deluxe sedan', 'volkswagen type 3', 'volkswagen 411 (sw)', 'volkswagen dasher', 'vw dasher', 'vw rabbit', 'volkswagen rabbit', 'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245', 'volvo 264gl', 'volvo diesel', 'volvo 246']\n",
      "\n",
      "toyota celica gt 的竞品车型是:\n",
      "['subaru', 'subaru dl', 'subaru brz', 'subaru r1', 'subaru trezia', 'toyota corona mark ii', 'toyota corona', 'toyota corolla 1200', 'toyota corona hardtop', 'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii', 'toyota corolla', 'toyota corolla liftback', 'toyota celica gt', 'vokswagen rabbit', 'volkswagen model 111', 'volkswagen super beetle', 'volkswagen rabbit custom']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for key in toyota_dict.keys():\n",
    "    competitve_car = result[result['class']==toyota_dict[key]]['CarName'].unique().tolist()\n",
    "    print(key,'的竞品车型是:')\n",
    "    print(competitve_car)\n",
    "    print()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import davies_bouldin_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29c9c2c9e88>]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = []\n",
    "for k in range(2, 10):\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(train_data)\n",
    "    score = davies_bouldin_score(train_data, kmeans.labels_)\n",
    "    scores.append(score)\n",
    "plt.plot(range(2,10),scores, '-o')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6类得分最高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
